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Automatic Gaussian Mixture Model (GMM) for segmenting 18F-FDG-PET images based on Akaike Information Criteria

Citation:

Tafsat A, Hadjili ML, Hafdaoui H, Bouakaz A, Benoudjit N. Automatic Gaussian Mixture Model (GMM) for segmenting 18F-FDG-PET images based on Akaike Information Criteria, in 2015 4th International Conference on Electrical Engineering (ICEE). Boumerdes, Algeria: IEEE ; 2015.

Abstract:

Positron emission tomography (PET) plays an important role in early tumour recognition, diagnosis and treatment. Automated and more accurate biological tumour volume (BTV) detection and delineation from PET is challenging. In this paper, we proposed a new method to segment (BTV) in 18 F-FDG-PET images using an automatic Gaussian mixture model (GMM) based on Akaike information criteria (AIC). The algorithm has been validated on two patients from seven had laryngeal tumours. The volumes estimated were compared with the macroscopic laryngeal specimens in which a 3-D biological tumour volume (BTV) defined by histology served as reference. Experimental results demonstrated that our method was able to segment the (BTV) more accurately than other threshold-based methods.

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